We have created an open‐source 3D printable microscope automatic stage and integrated camera system capable of providing a means for imaging microscope slides—the PiAutoStage. The PiAutoStage was developed to interface with the high‐quality optics of existing microscopes by creating an adaptable system that can be used in conjunction with a range of microscope configurations. The PiAutoStage automatically captures the entire area of a microscope slide in a series of overlapping high‐resolution images, which can then be stitched into a single panoramic image. We have demonstrated the utility of the PiAutoStage when attached to a transmitted light microscope by creating high‐fidelity image stacks of rock specimens in plane polarized and cross‐polarized light. We have shown that the PiAutoStage is compatible with microscopes that do not currently have a camera attachment by using two different optical trains within the same microscope: one set of imagery collected through the photography tube of a trinocular microscope, and a second set through a camera mounted to an ocular. We furthermore establish the broad adaptability of the PiAutoStage system by attaching it to a reflected light stereo dissection microscope to capture images of microfossils. We discuss strategies for the online delivery of these large‐sized images in a data efficient manner through the application of tiled imagery and open‐source Java‐based web viewers. The low cost of the PiAutoStage system, combined with the data‐efficient mechanisms of online delivery make this system an important tool in promoting the universal accessibility of high‐resolution microscope imagery.
Confocal microscopy is a standard approach for obtaining volumetric images of a sample with high axial and lateral resolution, especially when dealing with scattering samples. Unfortunately, a confocal microscope is quite expensive compared to traditional microscopes. In addition, the point scanning in confocal microscopy leads to slow imaging speed and photobleaching due to the high dose of laser energy. In this paper, we demonstrate how the advances in machine learning can be exploited to teach a traditional wide-field microscope, one that’s available in every lab, into producing 3D volumetric images like a confocal microscope. The key idea is to obtain multiple images with different focus settings using a wide-field microscope and use a 3D generative adversarial network (GAN) based neural network to learn the mapping between the blurry low-contrast image stacks obtained using a wide-field microscope and the sharp, high-contrast image stacks obtained using a confocal microscope. After training the network with widefield-confocal stack pairs, the network can reliably and accurately reconstruct 3D volumetric images that rival confocal images in terms of its lateral resolution, z-sectioning and image contrast. Our experimental results demonstrate generalization ability to handle unseen data, stability in the reconstruction results, high spatial resolution even when imaging thick (∼40 microns) highly-scattering samples. We believe that such learning-based microscopes have the potential to bring confocal imaging quality to every lab that has a wide-field microscope.
more » « less- NSF-PAR ID:
- 10369352
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Biomedical Optics Express
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2156-7085
- Page Range / eLocation ID:
- Article No. 284
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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